An artificial neural network to assess the impact of neighbouring photovoltaic systems in power forecasting in Utrecht, the Netherlands

Abstract

In order to perform predictions of a photovoltaic (PV) system power production, a neural network architecture system using the Nonlinear Autoregressive with eXogenous inputs (NARX) model is implemented using not only local meteorological data but also measurements of neighbouring PV systems as inputs. Input configurations are compared to assess the effects of the different inputs. The added value of the information of the neighbouring PV systems has demonstrated to further improve the accuracy of predictions for both winter and summer seasons. Additionally, forecasts up to 1 month are tested and compared with a persistence model. Normalized root mean square errors (nRMSE) ranged between 9% and 25%, with the NARX model clearly outperforming the persistence model for forecast horizons greater than 15min.